Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram.
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ABSTRACT:
SUBMITTER: Chen D
PROVIDER: S-EPMC7794254 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram.
Chen Dexin D Liu Zhangyuanzhu Z Liu Wenju W Fu Meiting M Jiang Wei W Xu Shuoyu S Wang Guangxing G Chen Feng F Lu Jianping J Chen Hao H Dong Xiaoyu X Li Guoxin G Chen Gang G Zhuo Shuangmu S Yan Jun J
Nature communications 20210108 1
Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a h ...[more]
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Project description:ImportanceOccult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis.ObjectiveTo assess the use of a deep learning model for predicting occult peritoneal metastasis based on preoperative computed tomography images.Design, setting, and participantsIn this multicenter, retrospective cohort study, a deep convolutional neural network, the Peritoneal Metastasis Network (PMetNet), was trained to predict occult peritoneal metastasis based on preoperative computed tomography images. Data from a cohort of 1225 patients with gastric cancer who underwent surgery at Sun Yat-sen University Cancer Center (Guangzhou, China) were used for training purposes. To externally validate the model, data were collected from 2 independent cohorts comprising a total of 753 patients with gastric cancer who underwent surgery at Nanfang Hospital (Guangzhou, China) or the Third Affiliated Hospital of Southern Medical University (Guangzhou, China). The status of peritoneal metastasis for all patients was confirmed by pathological examination of pleural specimens obtained during surgery. Detailed clinicopathological data were collected for each patient. Data analysis was performed between September 1, 2019, and January 31, 2020.Main outcomes and measuresThe area under the receiver operating characteristic curve (AUC) and decision curve were analyzed to evaluate performance in predicting occult peritoneal metastasis.ResultsA total of 1978 patients (mean [SD] age, 56.0 [12.2] years; 1350 [68.3%] male) were included in the study. The PMetNet model achieved an AUC of 0.946 (95% CI, 0.927-0.965), with a sensitivity of 75.4% and a specificity of 92.9% in external validation cohort 1. In external validation cohort 2, the AUC was 0.920 (95% CI, 0.848-0.992), with a sensitivity of 87.5% and a specificity of 98.2%. The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors (AUC range, 0.51-0.63). In multivariable logistic regression analysis, PMetNet was an independent predictor of occult peritoneal metastasis.Conclusions and relevanceThe findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.
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Project description:BackgroundPeritoneal metastasis (PM) in gastric cancer (GC) remains an untreatable disease, and is difficult to diagnose preoperatively. Here, we aim to establish a novel prediction model.MethodsThe clinicopathologic characteristics of a cohort that included 86 non-metastatic GC patients and 43 PMGC patients from Zhongshan Hospital were retrospectively analysed to identify PM associated variables. Additionally, mass spectrometry and glycomic analysis were applied in the same cohort to find glycomic biomarkers in serum for the diagnosis of PM. A nomogram was established based on the associations between potential risk variables and PM.ResultsOverexpression of 4 N-glycans (H6N5L1E1: m/z 2620.93; H5N5F1E2: m/z 2650.98; H6N5E2, m/z 2666.96; H6N5L1E2, m/z 2940.08); weight loss ≥ 5 kg; tumour size ≥ 3 cm; signet ring cell or mucinous adenocarcinoma histology type; poor differentiation; diffuse or mixed Lauren classification; increased CA19-9, CA125, and CA724 levels; decreased lymphocyte count, haemoglobin, albumin, and pre-albumin levels were identified to be associated with PM. A nomogram that integrated with five independent risk factors (weight loss ≥ 5 kg, CA19-9 ≥ 37 U/mL, CA125 ≥ 35 U/mL, lymphocyte count < 2.0 * 10 ~ 9/L, and H5N5F1E2 expression ≥ 0.0017) achieved a good performance for diagnosis (AUC: 0.892, 95% CI 0.829-0.954). When 160 was set as the cut-off threshold value, the proposed nomogram represented a perfectly discriminating power for both sensitivity (0.97) and specificity (0.88).ConclusionsThe nomogram achieved an individualized assessment of the risk of PM in GC patients; thus, the nomogram could be used to assist clinical decision-making before surgery.
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Project description:BACKGROUND: Pulmonary metastasis (PM) following curative hepatectomy for hepatocellular carcinoma (HCC) is indicative of a poor prognosis. This study aimed to develop a nomogram to identify patients at high risks of PM. METHODS: A primary cohort of patients who underwent curative hepatectomy for HCC at the Eastern Hepatobiliary Surgery Hospital from 2002 to 2010 was prospectively studied. A nomogram predicting PM was constructed based on independent risk factors of PM. The predictive performance was evaluated by the concordance index (c-index), calibration curve and decision curve analysis (DCA). During the study period, a validation cohort was included at the First Affiliated Hospital of Fujian Medical University. RESULTS: Postoperative PMs were detected in 106 out of 620 and 45 out of 218 patients, respectively, in two cohorts. Factors included in the nomogram were microvascular invasion, serum alpha-fetoprotein, tumour size, tumour number, encapsulation and intratumoral CD34 staining. The nomogram had a c-index of 0.75 and 0.82 for the two cohorts for predicting PM, respectively. The calibration curves fitted well. In the two cohorts, the DCA demonstrated positive net benefits by the nomogram, within the threshold probabilities of PM >10%. CONCLUSION: The nomogram was accurate in predicting PM following curative hepatectomy for HCC.
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